aws

The AWS ML Engineer staple. The answer that scores is a clean division of labor, CodePipeline owns code, SageMaker Pipelines owns the ML DAG, the Model Registry is the handoff point, plus the cross-account detail that separates real builds from doc reading.

The 'why' is the question: AWS gives you five ways to host a model, and interviewers want the decision tree plus the trade-off you knowingly accepted. The walkthrough structure, the real cost math between SageMaker endpoints and EKS, and the answer shape that survives drilling.

A staff-level platform design where the constraint set, not the architecture, is the test. The controls SR 11-7 actually demands, how to make audit a byproduct of the pipeline instead of a quarterly fire drill, and the v1 you can ship without a compliance finding.